Journal of Mathematics and Statistics 5 (4): 287-297, 2009 ISSN 1549-3644 © 2009 Science Publications

Assessing Computer Knowledge in Senior High School: A Case Study of the in

1Oladejo, N.K. and 2I.A. Adetunde 1Department of Applied Mathematics and Computer Science, University for Development Studies, , Ghana 2Department of Mathematics, University of Mine andTechnology Tarkwa, Ghana

Abstract: Problem statement: This study contracted with assessing the knowledge of computer in Senior High Schools of the Upper East Region in Ghana. Approach: Data collected by means of administration of questionnaire brings out the level of computer knowledge expected from a student completing a Senior High School in the Upper East Region in Ghana. 140 sample students from 8 districts were selected for the study. Results: Hypotheses were tested at α = 0.05 while the analyses of data were presented through SAS and SPSS. General Linear Model (GLM), post ANOVA and Least Significant Difference (LSD) were also used. Conclusion: The study revealed that an average student in Senior High School of the Upper East Region can not pass in computer subject. The research further revealed that gender has no influence on the level of computer knowledge of a student. This indicates that the level of computer knowledge of males and females is not significantly different.

Key words: Computer knowledge, senior high school, variance, ANOVA, grade, hypothesis, percentage, gender

INTRODUCTION with no computer knowledge can still make a very good grade in integrated science. Computers are powerful educational tools that Looking at the importance of computers in this modern when properly employed will undoubtedly transform World has necessitated the need to assess the level of Ghana educational system by changing the contents of computer knowledge a student completing senior high education and the nature of learning processes in Senior school acquires before completion. Secondary Schools in Ghana. Computers have the This study then wish to determine the level of potential to help students to solve problems, think for computer knowledge a senior high school student themselves and collaborate with others. Some critics of acquires before completion, establish the differences in computer knowledge of senior high schools students educational technology have drawn conclusions for across districts, establish if any of the differences in current educational technology based upon previous computer knowledge between males and females of technologies. Others have argued that technology will senior high schools, establish if the presence of a never influence learning Oppenheimer , Postman, Tyack [1,3-5] computer laboratory has got an influence on level of and Cuban, Clark . These criticisms have been the computer knowledge in senior high schools and lastly catalyst for research into instructional methods and generate data for future research. educational technology, as they must be linked by the [2] very nature of education Kozma . Computer science is Research hypothesis: Attempts were made to test the a sub set of integrated science which is made up of following hypotheses: introductory biology, physics, agriculture, chemistry and computer in senior high schools in Ghana. Hypothesis one: Districts versus Districts Students completing senior high schools are Ho1 : There is no significant difference in the level of therefore not fully assessed on basic computer computer knowledge across districts of the Upper East knowledge as it forms a very small integral part of their Region. external assessment in integrated science. A student Hypothesis Two: Males versu s Females Corresponding Author: Oladejo, N.K., Department of Applied Mathematics and Computer Science, University for Development Studies, Navrongo, Ghana 287 J. Math. & Stat., 5 (4): 287-297, 2009

[9,10] Ho2 : There is no significant difference in the level of inequalities in ICT distribution ; lack of information computer knowledge between Males and Females. regarding the distribution of ICT; low levels of literacy Hypothesis three: Schools with computer laboratory in general and lack of relevant content and technology versus Schools without computer laboratory applications to meet the needs of diverse societies[12,14] . Ho3 : Students from schools with computer laboratory Educational policy makers, Non-Governmental and student from schools without computer laboratory Organizations (NGO), bilateral and multilateral donor have the same level of computer knowledge. organizations and school administrators are making the collective efforts to promote ICT in Ghanaian Historical background on introduction of computers secondary schools. Because of the efforts of NGOs and and ICT into senior secondary schools (senior high donor organizations in particular, ICT facilities have schools) in Ghana: The application of Information and extended to some schools. Communication Technology (ICT) in schools is The National ICT Policy and Plan Development perceived as a means for transforming teaching and Committee met with the President and members of the learning processes and has thus been met with significant National House of Chiefs on Thursday, the 28th of enthusiasm. The developing world also perceives ICT as November 2002 as part of the National Consultative a tool that will promote socioeconomic, political and exercise aimed at developing an ICT-driven socio- sustainable development. This perception underpins the economic development policy and plan. During the introduction of computers and the internet in some meeting some of the key comments and observations secondary schools in Ghana. made by chiefs are the following: Computer literacy is not an optional luxury but a There is no doubt that ICT could play a major role fundamental part of the development needs of the in facilitating Ghana’s development. There is a need for school and of the country as a whole. It is a key to the the country to embrace ICT if it is to achieve its goal of improving performance on the West African developmental goals in the new information age. Examination Council (WAEC) exams, notably in Math, The Ghana ICT for accelerated development Integrated science and English. It can be a useful process is extremely important and this could lead to educational tool for all areas of the syllabus. the reconstruction of the economy. Computer literacy advances best not by copying The nation’s successes in pursuing an ICT-led what others have done elsewhere but in finding development agenda will to a large extend depend on solutions appropriate to local circumstances. So for the youths. The youths should therefore be equipped example, instead of training students on one particular with the necessary skills to help them develop and word processor such as Microsoft Word, the goal is to move the nation forward. Basic literacy of the give students a broad based understanding of word population will be playing a key role in the ICT for processors and the Graphical User Interfaces (GUIs) development process. There is therefore the need to behind them. Computer literacy should be a hands-on activity. The students’ primary focus of attention should address issues relating to the illiteracy rate of the be on the computers, not on the instructor. country. The countries that have developed made it Education policymakers in Ghana have hailed the partly through high literacy rates of their populations. introduction of Information and Communication The Malaysian and Singaporean success story can to Technology (ICT) in Ghanaian secondary schools as a some extent be attributed to those nation’s high literacy remarkable step that will contribute to knowledge rates and skilled manpower. production, communication and information sharing among students and teachers in the school system. This Population of the study: The research covered seven perception stems from assertions in the literature about [13,14] districts in the Upper East Region. Name of selected the benefits that come with ICT literacy in schools school, district and location in each region are shown in points out that ICT is a transformative tool and its full Table 2, in which the total population is 140 students. integration into the school systems is necessary to prepare students for the information society they will inherit. Contrary to the promising notion of ICT as a Sampling techniques: Two sampling techniques means of knowledge production, numerous scholars (stratified sampling and multistage sampling) were used have highlighted the need to address the numerous in the sampling process. The whole sampling process problems that the introduction of ICT will bring. was randomly done. No particular school was chosen These issues include: lack of adequate planning for for a particular purpose. All schools were considered implementation of ICT [7]; inadequate teacher training, equal in terms of infrastructures and teachers. 288 J. Math. & Stat., 5 (4): 287-297, 2009

Instrumentation: Only primary data was collected as mater in terms of computer teaching in Senior High there was no past record on the performance of students Schools, especially in the Upper East Region should in computer in schools. This was due to the fact that sit-up. computer was not an independent subject in Senior The general performance of the 140 students High Schools. The primary data was collected using an sampled in term of the new West African Senior School appropriate designed questionnaire. Certificate Examination (WASSCE) grading system, is displayed in Table 9. Validation of the instrument: The selection of the From Table 9 below, as much as 107 students schools and the students was purely random. All representing 76.4% of the total sample size failed, by schools had equal opportunity of being selected. All the new West African Senior School Certificate form three students irrespective of their course and Examination (WASSE) grading system. This leaves class had equal chances of selection. The sampling unit only 33 students representing 23.6% to be distributed was obtained by just randomly calling any form three among the other grades. The distribution of the grades students. across the districts is shown on the Table 9. From the Table 10 below indicates that the best Data analysis procedure: Data collected was coded performance came from the - District and entered into SAS and SPSS where all the analyses with the worse performance coming from the Builsa were done. All hypothesis were tested at the 95% district as all students sampled from this district failed. confident interval (Significant level of α = 0.05). If the The detailed performances with regard to the various calculated significance is less than the α = 0.05, then we variables are discussed below. fail to accept the null hypothesis (H o), otherwise the Performance across districts: A total of seven alternative hypothesis (H 1) is rejected in favor of the null hypothesis. districts were considered in the research process. The The tools that will be used during the analysis will mean marks scored by student in the various districts include Analysis of Variance (ANOVA), Student T- are displayed in Table 2. Test and Post ANOVA. From the Table 2 it can be seen that Talensi- Nabdam District recorded the highest mean score followed by Kasena-Nankana District with Builsa General description of variables: The data collected were coded and entered into excel. It was then District scoring the least. transported into SPSS and SAS where appropriate By the new West African Senior School Certificate Examination (WASSCE) grading system, in exception analyses were made. The data collected was coded of Talensi-Nabdam district where an average SHS under 13 variables. The variables and their respective student can score grade C (Credit), an average SHS number of levels and meaning are displayed in the 6 student from the other six districts will score grade F Table 12 data below. 9 (Failure).

Description of variables: The marks obtained by Performance based on gender: The mean mark score individual students in the test conducted during the of males and females are displayed in Table 1 below. data collection process were marked out of 100. The new West African Senior School Certificate Table 1: Marks of students * sex of student Examination (WASSCE) grading system is displayed in Sex of student Mean mark Number Table 9 and 10 below. Most analysis will be in Male 32.77 70 reference to this grading system. (Source: Female 28.57 70 http://www.ghanawaec.org) Total 30.67 140

Table 2: Marks of students * district Preliminary analysis: District Mean mark Number General performance: The research conducted Talensi-Nabdam District 55.40 20 revealed that the average Senior High School student in Municipal 26.10 20 the Upper East Region will score about 31% which by 27.75 20 the new West African Senior School Certificate West District 25.45 20 Bawku Municipal 22.65 20 Examination (WASSCE) grading system is equivalent Kasena-Nankana District 39.90 20 to F 9 (Failure). This is an indication that the general Builsa District 17.45 20 performance was very poor and all stakeholders who Total 30.67 140 289 J. Math. & Stat., 5 (4): 287-297, 2009

Table 3: Marks of students * computer usage Table 5: Marks of students * practical work Computer usage Mean mark Number Practical work Mean mark Number Yes 35.87 94 Yes 37.29 80 No 20.04 46 No 21.85 60 Total 30.67 140 Total 30.67 140

Table 4: Marks of students * computer lab Table 6: Marks of students * no. of computer teachers Computer lab Mean mark Number No. of computer teachers Mean mark Number Yes 34.36 100 0 17.45 20 No 21.45 40 1 27.75 20 Total 30.67 140 2 24.05 40 4 47.65 40 5 26.10 20 Table 1 reveals that the mean mark scored by Total 30.67 140 males is slightly higher than that of females even though their respective mean scores will both yield a Performance based on practical lessons: During the research it came out that even though some schools had grade of F 9 (Failure). This is an indication that the general performance of both males and females in a computer laboratory, they did not give their student Senior High School in computer is very poor. the opportunity to visit the laboratory for practical lessons. Table 5 below gives a frequency distribution

and their respective categorical mean mark scores. Frequency distribution of students based on There is an indication that 57% of the 140 students whether they have ever used a computer before: sampled have practical lessons as against 43% who did Students were asked whether they have ever used a not have practical lessons. Comparing the percentage computer before. Their frequency distribution based on number of students, who had practical lessons to the the responses they gave is displayed in Table 3. percentage number of students that had access to a In the Table 3, it is clear that about 67% of the computer laboratory, shows that it had reduced from 72 students sampled out for this research have ever used a to 57%. This is a clear indication that some students are computer as against 33% who had never used the been denied their right to practical lessons even though computer. they paid to use the facility. The performance of the students in each category is displayed as shown in Performance based on accessibility to computer Table 5. laboratory: Students were asked whether they had a Performance of both categories of students was computer laboratory or not. Table 4 display their poor as can be seen from Table 5. An average student frequency distributions and their respective mean mark who attends practical computer lesson will score about scores. 37% which will yield grade F 9 (Failure). The least said Table 4 indicates that out of the 140 students about the students who did not practical lessons the sampled, 100 of them representing about 72% had better. Table 5 and 9 give reasons to believe that having access to a computer laboratory while the remaining practical lessons will improve a student’s knowledge in 40 students representing about 28% did not have a computer. This may be due to the assertion by many computer lab in their respective schools. Their mean people that ‘practice makes man perfect’. marks in term of accessibility are displayed in Table 4. Performance based on the number of computer teachers: Students were asked to indicate the number Table 4 shows that students who have access to of computer teachers they had in their respective computer laboratory perform slightly better than schools. Their responses are displayed in Table 6 students with no access to a computer laboratory. Even below. Their frequencies and average mark score of though the mean mark scored by either category of each category are put together on the same chart. students is nothing to write home about, there is still a From Table 6, it can be seen that 40 students had 2 possible indication that availability of computer teachers in their school and another 40 had 4 teachers in laboratory to students may be a contributive factor to their schools. 20 students did not have a computer their knowledge in computer. This will be tested in the teacher in their school. The highest mean mark score further analysis to verify if the availability of computer came from the category of students who had 4 computer laboratories in schools is a real factor of computer teachers, with the lowest mean mark score recorded knowledge. by the category of students with no computer teacher. It 290 J. Math. & Stat., 5 (4): 287-297, 2009 might be expected that the more the number of computer Further analysis: During the preliminary analysis it teachers in a school, the better the knowledge they was suspected that some factors were responsible for impact as the teacher to student ratio will be low. But it the poor performance. This section will seek to further must also be noted that individual differences among the analyze these factors to confirm or disprove the teachers, in term of method, skill and knowledge are suspected claims such as males perform better than possible factors that may sway this pattern. females, category of students who have access to a computer laboratory perform better than the category of Performance based on time spent per practical students who do not have access to a computer lesson: Table 7 displays the performance of students laboratory and that the district in which a student based on the amount of time spent per practical lesson. attends school has an influence on his/her performance. It is believed that the more time spent on practical There were numerous factors that were suspected to lessons the better the performance of students in have an influence on performance of students in computer. It is therefore not surprising that this pattern computer as discussed in the preliminary analysis. But has been displayed by the above chart with the category for the purpose of this study, only the availability of a of students who spent one hour per practical lesson computer laboratory, gender of the student and the recording the highest mean mark while the category of district to which a student belongs will be considered. students who do not have practical lessons scored the least mean mark. By the new West African Senior Table 7: Marks of students * hours per visit School Certificate Examination (WASSCE) grading Hours per visit Mean mark Number 30 min 33.83 40 system, an average student who spends one hour per 1 h 40.75 40 practical lesson will score grade E 8 (pass). This could Others 21.85 60 be an indication that the amount of time spent per Total 30.67 140 practical lessons had an influence on the general performance in computer. Table 8: Marks of students * ratio of student to computer Ratio of student to computer Mean mark Number 1-to-2 41.58 40 Performance based on number of students per a 1-to-5 33.00 40 computer: Students were required to indicate the Others 21.85 60 number of them that use one computer at a time during Total 30.67 140 practical lessons. Their frequency distribution and mean mark score for each category is displayed on the Table 8. Table 9: Grade of students (remark) The pattern indicated by Table 8 shows that the Grade (remark) Frequency Percent Valid (%) Cumulative (%) lower the ratio the better the performance as the mean F9 (fail) 107 76.4 76.4 76.4 E (pass) 10 7.1 7.1 83.6 mark score for the category of students who sit two to a 8 D7 (pass) 4 2.9 2.9 86.4 computer is the highest. As usual the mean mark of C6 (credit) 6 4.3 4.3 90.7 students who do not have practical lessons is lowest. C5 (credit) 3 2.1 2.1 92.9 This could be due to the fact that as the ratio becomes C4 (credit) 3 2.1 2.1 95.0 B (good) 1 0.7 0.7 95.7 smaller, it gives more opportunity to the students to 3 B2 (very good) 2 1.4 1.4 97.1 have access to the computer thereby increasing their A1 (excellent) 4 2.9 2.9 100.0 understanding. Total 140 100.0 100.0

Table 10: Grade of students (remark) * district cross tabulation Districts ------Grade of students Talensi-Nabdam Bolgatanga Bongo Bawku Bawku Kasena-nankana Builsa (Remark) District Municipal District District West District Municipal District Total F9 (fail) 5 18 17 18 17 12 20 107 E8 (pass) 2 1 3 2 2 10 D7 (pass) 2 1 1 4 C6 (credit) 2 1 3 6 C5 (credit) 1 1 1 3 C4 (credit) 3 3 B3 (good) 1 1 B2 (very good) 2 2 A1 (excellent) 3 1 4 Total 20 20 20 20 20 20 20 140

291 J. Math. & Stat., 5 (4): 287-297, 2009

Table 11: Grade of students (remark) * computer lab cross-tabulation Computer lab ------Grade of students (remark) Yes No Total F9 (fail) 69 38 107 E8 (pass) 10 10 D7 (pass) 4 4 C6 (credit) 5 1 6 C5 (credit) 3 3 C4 (credit) 3 3 B3 (good) 1 1 B2 (very good) 2 2 A1 (excellent) 4 4 Total 100 40 140

Table 12: Data Id Sex Age District RG of RES Use Comp Comp Lab Prt Wrks Hours per vist Ratio No. Comp Tchs Tchs Pfs Mark 120 1 18 1 UER 1 1 1 2 2 4 2 76 110 2 18 1 UER 1 1 1 2 2 4 3 27 112 1 19 1 UER 1 1 1 2 2 4 3 58 111 1 18 1 UER 1 1 1 2 2 4 2 88 113 1 20 1 BAR 1 1 1 2 2 4 1 81 114 1 20 1 UER 1 1 1 2 2 4 2 80 115 1 19 1 NR 1 1 1 2 2 4 4 48 116 1 17 1 UER 1 1 1 2 2 4 1 24 117 1 18 1 UER 1 1 1 2 2 4 2 60 118 1 20 1 UER 1 1 1 2 2 4 4 78 109 2 18 1 UER 1 1 1 2 2 4 2 42 108 2 19 1 UER 1 1 1 2 2 4 2 57 107 2 18 1 UER 1 1 1 2 2 4 1 25 106 2 18 1 ASR 1 1 1 2 2 4 2 51 105 2 19 1 UER 1 1 1 2 2 4 1 51 104 2 18 1 UER 1 1 1 2 2 4 2 66 103 2 19 1 UER 1 1 1 2 2 4 4 31 102 2 18 1 UWR 1 1 1 2 2 4 4 67 101 2 19 1 NR 1 1 1 2 2 4 1 31 119 1 19 1 UER 1 1 1 2 2 4 3 67 100 1 25 2 UER 1 1 1 2 3 5 2 39 81 1 22 2 UER 1 1 1 2 3 5 4 28 82 1 20 2 UER 1 1 1 2 3 5 5 1 83 2 19 2 UER 1 1 1 2 3 5 4 9 84 1 19 2 UER 1 1 1 2 3 5 5 31 85 2 18 2 UER 1 1 1 2 3 5 1 34 86 2 22 2 UER 1 1 1 2 3 5 4 9 87 2 17 2 UER 1 1 1 2 3 5 1 40 88 2 19 2 UER 1 1 1 2 3 5 2 15 89 2 19 2 UER 1 1 1 2 3 5 4 26 90 2 20 2 UER 1 1 1 2 3 5 1 15 91 1 25 2 UER 1 1 1 2 3 5 5 35 92 2 19 2 ASR 1 1 1 2 3 5 1 34 93 2 18 2 ASR 1 1 1 2 3 5 2 33 94 1 19 2 ASR 1 1 1 2 3 5 2 33 96 1 29 2 UER 1 1 1 2 3 5 1 33 97 1 19 2 UWR 1 1 1 2 3 5 2 54 98 1 24 2 UER 1 1 1 2 3 5 5 20 99 2 20 2 UWR 1 1 1 2 3 5 3 16 95 1 20 2 UER 1 1 1 2 3 5 3 17 80 2 18 3 UER 1 1 1 1 2 1 5 25 61 1 20 3 UER 1 1 1 1 2 1 5 33 62 1 18 3 UER 1 1 1 1 2 1 1 37 63 2 20 3 UER 1 1 1 1 2 1 3 43 64 2 20 3 UER 1 1 1 1 2 1 1 34 65 2 18 3 UER 1 1 1 1 2 1 5 39 66 1 24 3 UER 1 1 1 1 2 1 2 47 67 2 19 3 UER 1 1 1 1 2 1 5 10 292 J. Math. & Stat., 5 (4): 287-297, 2009

Table 12: Continued 68 1 20 3 UER 1 1 1 1 2 1 4 23 69 1 18 3 UER 1 1 1 1 2 1 3 12 70 1 18 3 UER 1 1 1 1 2 1 1 14 71 2 20 3 UER 1 1 1 1 2 1 5 27 72 2 21 3 UER 1 1 1 1 2 1 3 20 73 1 22 3 UER 1 1 1 1 2 1 5 47 74 1 17 3 UER 1 1 1 1 2 1 5 38 75 2 17 3 UER 1 1 1 1 2 1 3 34 76 2 23 3 UER 1 1 1 1 2 1 5 23 77 1 21 3 UER 1 1 1 1 2 1 5 17 78 1 20 3 UER 1 1 1 1 2 1 2 19 79 2 19 3 UER 1 1 1 1 2 1 5 13 41 2 18 4 UER 2 2 2 4 4 2 3 25 42 1 22 4 UER 2 2 2 4 4 2 1 59 43 1 18 4 UER 2 2 2 4 4 2 3 25 44 2 19 4 UER 2 2 2 4 4 2 3 20 45 1 23 4 UER 2 2 2 4 4 2 3 15 46 1 25 4 UER 2 2 2 4 4 2 1 10 47 1 24 4 UER 2 2 2 4 4 2 4 7 48 1 20 4 UER 1 2 2 4 4 2 2 71 49 2 18 4 UER 2 2 2 4 4 2 1 11 50 2 19 4 UER 2 2 2 4 4 2 2 24 51 1 19 4 UER 2 2 2 4 4 2 1 17 52 2 18 4 UER 2 2 2 4 4 2 2 30 53 2 19 4 ASR 1 2 2 4 4 2 1 12 54 1 22 4 UER 2 2 2 4 4 2 4 20 55 1 21 4 UER 2 2 2 4 4 2 3 31 56 2 19 4 UER 1 2 2 4 4 2 2 33 57 2 21 4 UER 1 2 2 4 4 2 3 21 58 2 18 4 UER 2 2 2 4 4 2 2 18 59 1 18 4 UER 1 2 2 4 4 2 5 36 60 2 17 4 UER 2 2 2 4 4 2 3 24 21 2 16 5 UER 1 1 2 4 4 2 2 31 22 1 20 5 UER 2 1 2 4 4 2 3 12 23 1 20 5 UER 2 1 2 4 4 2 4 7 24 2 16 5 UER 1 1 2 4 4 2 3 15 25 2 17 5 NR 1 1 2 4 4 2 5 9 26 2 17 5 UER 1 1 2 4 4 2 5 38 27 2 18 5 UER 2 1 2 4 4 2 4 9 28 1 18 5 UER 1 1 2 4 4 2 2 60 29 2 16 5 UER 1 1 2 4 4 2 2 32 30 1 21 5 UER 2 1 2 4 4 2 1 9 31 1 14 5 UER 2 1 2 4 4 2 5 26 32 2 16 5 UER 1 1 2 4 4 2 1 46 33 1 19 5 UER 2 1 2 4 4 2 1 8 34 1 19 5 UER 2 1 2 4 4 2 3 40 35 1 19 5 NR 2 1 2 4 4 2 5 7 36 1 19 5 UER 1 1 2 4 4 2 4 28 37 1 20 5 UER 2 1 2 4 4 2 3 16 38 2 17 5 UER 1 1 2 4 4 2 3 9 39 2 18 5 UER 2 1 2 4 4 2 4 17 40 2 16 5 UER 2 1 2 4 4 2 2 34 20 1 17 6 UER 1 1 1 1 3 5 3 58 19 1 15 6 UER 1 1 1 1 3 2 3 13 18 1 21 6 UER 1 1 1 1 3 4 4 33 17 1 19 6 NR 1 1 1 1 3 4 4 23 16 1 20 6 CR 1 1 1 1 3 4 4 81 15 1 19 6 UER 2 1 1 1 3 4 5 36 14 1 22 6 UER 1 1 1 1 3 4 3 34 13 1 18 6 UER 1 1 1 1 3 4 3 13 12 1 19 6 UER 1 1 1 1 3 4 5 51 11 2 16 6 UER 2 1 1 1 3 4 4 36 10 2 20 6 GR 2 1 1 1 3 4 5 33 9 2 18 6 UER 1 1 1 1 3 4 4 41 8 2 18 6 UER 1 1 1 1 3 4 1 587 2 17 6 UER 1 1 1 1 3 4 2 55 6 2 21 6 UER 1 1 1 1 3 4 4 44 5 2 17 6 UER 1 1 1 1 3 4 5 63 4 2 18 6 UER 1 1 1 1 3 4 1 34 293 J. Math. & Stat., 5 (4): 287-297, 2009

Table 12: Continued 3 2 17 6 UER 2 1 1 1 3 4 4 34 2 1 23 6 UER 1 1 1 1 3 4 1 33 1 2 19 6 UER 1 1 1 1 3 4 2 25 140 1 19 7 UER 2 2 2 4 4 0 0 13 139 2 20 7 UER 1 2 2 4 4 0 0 28 138 2 19 7 UER 2 2 2 4 4 0 0 15 137 1 18 7 UER 1 2 2 4 4 0 0 20 136 1 19 7 UER 1 2 2 4 4 0 0 14 135 2 17 7 UER 2 2 2 4 4 0 0 12 134 2 19 7 UER 2 2 2 4 4 0 0 12 133 2 18 7 UER 2 2 2 4 4 0 0 25 132 2 17 7 UER 2 2 2 4 4 0 0 20 131 2 21 7 UER 2 2 2 4 4 0 0 13 130 1 21 7 UER 2 2 2 4 4 0 0 22 129 1 22 7 UER 2 2 2 4 4 0 0 30 128 2 18 7 UER 2 2 2 4 4 0 0 12 127 1 18 7 UER 2 2 2 4 4 0 0 17 126 1 20 7 UER 2 2 2 4 4 0 0 6 125 1 19 7 UER 2 2 2 4 4 0 0 14 124 2 19 7 UER 1 2 2 4 4 0 0 25 123 1 19 7 UER 2 2 2 4 4 0 0 8 122 1 19 7 UER 2 2 2 4 4 0 0 33 121 2 18 7 UER 2 2 2 4 4 0 0 10

Test of hypothesis: All hypotheses were tested using confirming the fact that the district in which a student is ANOVA in the General Linear Model procedure schooling has an effect on his/her performance in (GLM). The level of significance is α = 0.05. computer. This pattern was expected as there is no parity in terms of infrastructure and development in the Rejection region: Reject the null hypothesis in favor of districts. the alternative hypothesis if the probability associated with the F-value (pr > F) is less than α = 0.05, Post ANOVA: Since there was an indication of a otherwise we accept the null hypothesis. difference, another analysis was performed to find which districts were similar to each other in terms of Hypothesis one: Districts versus districts: performance. The option used for this test was Least Ho: There is no significant difference in the level of Significant Difference (LSD) under the comparison of computer knowledge across districts of the Upper East means. The result of the analyses is displayed below. Region. Output 4.1 H1: There is difference in the level of computer The SAS System knowledge across districts in Upper East Region. The GLM Procedure t-Tests (LSD) for MARK Result: Alpha 0.05000 Output 4.0 Error degrees of freedom 133.00000 The SAS System Error means square 221.80900 The GLM Procedure Critical value of t 1.97796 Dependent variable: MARK Least significant difference 9.31550 Sum of Means with the same letter are not significantly different Source DF Squares Mean square F-value Pr>F Model 6 19850.28571 3308.38095 14.92 <0.0001 t Grouping Error 133 29500.60000 221.80902 Mean N DISTRICT Corrected 139 49350.88571 A 55.400 20 Talensi-Nabdam Total B 39.900 20 Kasena-Nankana District C 27.750 20 Bongo District Source DF Type III SS Mean Square F-value Pr > F C District 6 19850.28571 3308.38095 14.92 <0.0001 D C 26.100 20 Bolgatanga Municipal D C Interpretation: From the result displayed above, the D C 25.450 20 α D C Pr>F is less than = 0.05 (that is 0.0001<0.05). This D C 22.650 20 Bawku Municipal means that we do not have enough evidence to accept D the null hypothesis. This is therefore an indication D 17.450 20 Builsa District 294 J. Math. & Stat., 5 (4): 287-297, 2009

Questionnaire: University for development studies: Faculty of applied sciences: Navrongo campus: Assessing computer knowledge in senior high schools: A case study of the upper east region: Please tick the box that is applicable to you Part A: Personal information

1. Sex Male Female 2. Age ………………… 3. Form …………………….. 4. Region of resident …………………….. 5. Have you ever used a computer? Yes No 6. At what age did you first use a computer? …………………. 7. At what level did you first use a computer? SHS JHS Primary Others (specify) …………………………

Part B: General assessment of computer teaching/facilities in the school 1. Do you have a computer lab in your school? Yes No 1b if no, go to no. 8 2. Do you visit the lab for practical works? Yes No 3. How often do you go there? Once a month Once every two week Once a week Others (specify) ………………. 4. How many hours do you spend per visit? 30 min 1 h 2 h Others (specify) ………………… 5. How many computers do you have in your lab? Total Functioning Malfunctioning 6. Is the number of computers enough to satisfy each class at a time? Yes No 7. How many of you use one computer at a time? 1-1 2-1 5-1 Other (specify) ………………………….. 8. How many computer teachers do you have? …………………….. 9. Is the number adequate for the whole school? Yes No 10. How would you grade the performance of your teacher(s) Excellent Very good Credit Good Poor 11. Do you have access to internet facilities in your school? 12. In your own view what do you think should be done to improve computer studies in your school? ......

Part C: General knowledge in computer 1. What is a computer? ……………………………………………………………………………………… ……………………………………………………….…………………………………………………………… ……………………..………………………………………………………………………………….. 2. Mention two uses of the computer i. ………………………………………………………………………… ii. ………………………………………………………………………… 3. Define the following: a) Input device ………………………………………………………………………………………………… …………………………………….. b) Output device ……………………………………………………...... ………………………………… ………………………………………………………………………………………………………………… 4. List two examples each of the following a) Input device: i……………………….. ii…………………………..... b) Output device: i………………………. ii…………………………….. c) Storage device: i………………………. ii…………………………….. 295 J. Math. & Stat., 5 (4): 287-297, 2009

5. State the full meaning of the following as applied to computer(s) a) RAM ……………………………………………………………...... b) ROM ……………………………………………………………………. c) CPU ……………………………………………………………………... 6. Write one function each of the following keys on the keyboard a) Arrow keys ……………………………………………………………….……………………………… …………………………………………. b) Space bar ………………………………………………………………. c) Caps lock ………………………………………………………………. d) Enter ……………………………………………………………… 7. Arrange the following in the correct order of opening a Microsoft word window; all programs, Microsoft office, start menu, Microsoft word. i) ……………………… ii) …………………….. iii) ………………… iv) ……………………... 8. Mention two Microsoft office tools. i ………………………….. ii …………………………… 9. Arrange the following in the correct order of saving a fresh document in Microsoft office; file name, save as, file, save. i) ……………… ii) ………………… iii) …………………… iv) ………….. 10. State the systematic order of shutting down a computer. ……………………………………………………………………………………………………………………… ……………………………………………………………………………………………………………………… …………..

Thank you

Interpretation: From the result above it can be seen Interpretation: From the result displayed above, the that the performance of students from Talensi-Nabdam Pr>F is greater than α = 0.05 (that is 0.1883>0.05). This district differs from the rest of the districts. The same means that we do not have enough evidence to reject the applies to Kassena-Nankana district. Meanwhile the null hypothesis that there is no significant difference in performance of students from Bongo District, the level of computer knowledge between Males and Balgatanga Municipal, Bawku West District and Females. We can therefore say that the knowledge of a Bawku were not significantly different. The same student is independent on the gender of the student. In applies to Balgatanga Municipal, Bawku West District, other words, it implies that you do not have to be a male or female to have in-depth knowledge in computer. Bawku Municipal and Builsa District. Hypothesis three: Schools with computer laboratory Hypothesis two: Males versus females: versus schools without computer laboratory: Ho: There is no significant difference in the level of Ho: Students from schools with computer laboratory computer knowledge between Males and Females. and student from schools without computer laboratory have the same level of computer knowledge. H1: Males have a higher knowledge in computer than females. H1: Students from schools with computer laboratory have higher computer knowledge than students from Result schools without computer laboratory ( Table 11). Output 4.2 Result The SAS System Output 4.3 The GLM Procedure The SAS System Dependent Variable: MARK The GLM Procedure Sum of Dependent Variable: MARK Source DF Squares Mean square F-value Pr > F Sum of Model 1 617.40000 617.40000 1.75 0.1883 Source DF Squares Mean Square F-value Pr > F Error 138 48733.48571 353.14120 Model 1 4761.94571 4761.94571 14.74 0.0002 Corrected 139 49350.88571 Error 138 44588.94000 323.10826 Total Corrected 139 49350.88571 Total

Source DF Type III SS Mean square F-value Pr > F Source DF Type III SS Mean Square F-value Pr > F SEX 1 617.4000000 617.4000000 1.75 0.1883 COMP- Lab 1 4761.945714 4761.945714 14.74 0.0002 296 J. Math. & Stat., 5 (4): 287-297, 2009

Interpretation: From the result displayed above, the 2. Kozma, R., 1994. Will media influence learning? Pr>F is less than α = 0.05 (that is 0.0002<0.05). This Reframing the debate. Educ. Tech. Res. Dev., means that we do not have enough evidence to accept the 42: 7-19. null hypothesis that Students from schools with computer 3. Oppenheimer, T., 1997. The computer delusion. laboratory and student from schools without computer Atlant. Monthly, pp: 45-62. laboratory have the same level of computer knowledge. 4. Postman, N., 1995. The end of education: In other words we are confirming that students from Redefining the Value of School. Alfred A. Knoph schools with computer laboratory will have more Random House, Inc., 400 Hahn Road, knowledge in computer than those students from schools Westminster, MD 21157 ($22), New York, pp: without computer laboratory. This may be due to fact 220. that students from schools with computer laboratory will http://eric.ed.gov/ERICWebPortal/custom/portlets/ have the chance to undertake practical lessons that will recordDetails/detailmini.jsp?_nfpb=true&_&ERIC go a long way to enhance their understanding. ExtSearch_SearchValue_0=ED392097&ERICExtS earch_SearchType_0=no&accno=ED392097 CONCLUSION 5. Tyack, D. and L. Cuban, 1995. Tinkering Towards Utopia. A Century of Public School Reform. This study statistically assesses the knowledge of Harvard University Press, Cambridge, computer among Senior High School students in the Massachusetts, pp: 121-126 Upper East Region of Ghana. 6. Vikas Gupta, 2008. Secret Guide to Computers. The results of the research indicate that the general EPP Books Publisher, ISBN: 998807588x. performance of students in Senior High Schools of the 7. National ICT Policy and Plan Development Upper East Region in computer is very poor. The Committee report, 2002. average mark score (in the test included in the http://www.ict.gov.gh/html/press%20conference_la questionnaire) of the 140 sampled students from 7 unch.htm districts was 30.67 which is equivalent to F 9 (Failure). 8. Mooij, T. and Ed. Smeets, 2001. Modelling and And 77% of 140 sampled students failed with only 23% supporting ICT implementation in secondary pass. This means that an average student in Senior High schools. 36: 265-281. School of the Upper East Region can not pass in 9. Nachmias, R., D. Mioduser and A. Shemla, 2000. computer assuming it was a gradable subject. Internet usage by students in an Israeli high school. The research further revealed that gender has no J. Educ. Comput. Res., 22: 55-73. influence on the level of computer knowledge of a 10. Sutherland-Smith, W., I. Snyder and L. Angus, student. This means the level of computer knowledge of 2003. The digital divide: Differences in computer males and females is not significantly different. use between home and school in low socio- Meanwhile the district in which a student is attending economic households. L1-Educ. Stud. Language school and the availability of a computer laboratory to Literat., 3: 31-45. students had an influence on the level of computer 11. Snyder, I., L. Angus and W. Sutherland, 2002. knowledge of the students at α = 0.05 level of Building equitable literature futures: Home and significance. The difference in performance of students School computer-mediated literacy practices and across districts may due to the disparity of disadvantage. Cambridge J. Educ., 32: 368-383. infrastructure and development in the various districts. 12. Education Testing Services, 2001. Digital It was clear, that students from schools with computer transformation: A framework for ICT literacy. laboratory performed better than those from schools http://www.ets.org/research/ictliteracy/ictreport.pdf without computer laboratory. This was not surprising as 13. Mucherah, W.H, 2003. The influence of the students from schools with computer laboratory will technology on the classroom climate of social have practical lessons that will go a long way to studies classrooms: A multidimensional approach. increase their understanding as the saying goes Learn. Environ. Res., 6: 37-57. “practice makes perfect”. 14. Hakkarainen, K., E. Ryymin and T. Palonen, 2008. Networking relations of using ICT within a teacher REFERENCES community. Comput. Educ. J., 51: 1264-1282.

1. Clark, R., 1983. Reconsidering research on learning with media. Rev. Educ. Res., 53: 445- 459.

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